1. Field of the Invention
The invention is drawn to a method and apparatus for measuring plant canopy temperature and multi-spectral reflectance and managing irrigation.
2. Description of the Prior Art
Multi-band radiometers are optical sensors, typically consisting of two or more photodiode detectors or charge coupled devices, which produce electrical current that is proportional to the light energy to which they are exposed. The incoming light is filtered to a specific wavelength in the visible and near infrared ranges. These sensors are typically remote (as opposed to contact) sensors used as hand-held or vehicle-mounted instruments to remotely assess plant biophysical properties such as leaf area and ground cover (Vaesen et al., 2001, Field Crops Research, 69:13-25); crop nutrient status [Sui et al., 2005, Appl. Engr. Agric., 21(2):167-172; Sui and Thomasson, 2006, Trans. of the ASABE, 49(6):1983-1991; Shanahan et al., 2008, Computers and Electronics in Agric., 61(1):51-62]; and biotic stress (Mirik et al., 2006, Computers and Electronics in Agric., 51:86-98). There are a number of commercialized multi-band radiometers including the Crop Circle (Holland Scientific, Inc., Lincoln, Nebr.), CropScan (Crop Scan Inc., Rochester, Minn.), FieldSpec (Analytical Spectral Devices, Boulder, Colo.), and GreenSeeker (Ukiah, Calif.). These multi-band sensors include photodetectors in the visible and NIR infrared range.
The primary method of remote water stress detection has been through the use of the thermal part of the spectrum [Barnes et al., 2000, Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data (pdf file #13). In Proc. 5th Intl. Conf. on Precision Agriculture and Other Resource Management. Robert et al. eds. Madison, Wisc.:ASA-CSSA-SSSAJ]. Furthermore, thermal measurement observations obtained using remote sensors usually contain the temperatures of both vegetation and the soil background, particularly for row crops with partial cover. Colaizzi et al. [2003, J. Irrig. Drainage Engr., 129(1):36-43] used reflectance in the red and near-infrared bands to estimate fraction of vegetation cover within the field of view of an infrared thermometer. Since the soil is often a different temperature than the vegetation, it is important to distinguish the temperatures of each component when using thermal-based indices for irrigation automation.
The literature does refer to a multi-band radiometer, which incorporated a band in the thermal range. The hand-held instrument was a sensor constructed by Robinson et al. (1979, Multiband radiometer for field research. Society of Photo-Optical instrumentation Engineers, Bellingham, Vol. 196, pp. 8-15) for research use. In a technical note, it was reported by Jackson and Robinson (1985, Remote Sensing Environ., 17:103-108) to be a hand-held eight band radiometer with a lithium tantalate detector as the thermal sensor with filtering properties in the range of 10.5-12.5 μm. However, the technical note did not address measurements concerning the thermal band. The second reference to a multi-band radiometer with a thermal detector concerned a multi-spectral sensor developed at the U.S. Arid Land Agricultural Research Center in Maricopa, Ariz. The sensor is part of a remote sensing package installed on a moving irrigation system and was discussed briefly by Barnes et al. (2000, ibid); Colaizzi et al. (ibid); and Haberland et al. [2010, Appl. Engr Agric., 26(2): 247-253]. Measurements from this sensor were used to refine nitrogen and water status indices for cotton and broccoli. Although this sensor contained four spectral bands in the green, red, red-edge, and NIR regions, the detector for the infrared thermal region was a physically separate sensor as shown in Haberland et al. (2010, ibid) and wired to a datalogger.
A functional wireless infrared thermometer (IRT) was developed separately (O'Shaughnessy et al., 2011, Computers and Electronics in Agric., 76:59-68) and 32 were deployed onto a six-span center pivot lateral as a wireless network system in a topology similar to that described by O'Shaughnessy and Evett [2010, Appl. Engr. in Agric. 26(2):331-341] for an earlier wireless IRT prototype. Measuring crop canopy surface radiometric temperature remotely using such an IRT has the disadvantage of also measuring soil background reflectance early in a growing season when crop canopy cover is limited, and possibly throughout the growing season in the case of row crop planting (for sensor view angles that are nadir or substantially parallel to crop rows), missing plants, and changing leaf architecture from loss of turgor pressure.
Identification of the presence of plant disease or pest infestation is typically manifested by changes in plant pigments that cause a drift in healthy canopy reflectance measurements. Diseases can mainly be detected in the yellow-red reflectance and with a lower NIR reflectance (Moshou et al., 2011, Biosystems Engr., 108:311-321). This deviation is detectable with multi-band photoactive sensors with bandwidth in the visible region between 470 and 670 nm and was used to detect the spread of wheat streak mosaic virus [Workneh, et al., 2009, Phytopathology 99(4):432-440] and pest infestation (Mirik et al., 2006, Computers and Electronics in Agric. 51:86-98) in winter wheat. Qin and Zhang (2005, International J. Applied Earth Observation., 7:115-128) used ratio indices from bands in the visible and NIR range to detect rice sheath blight.
Since all colors are made up of a combination of red (R), green (G) and blue (B), a sensor that is capable of filtering light in these three bands possesses flexibility in detecting color changes in crop canopy. Reflectance data are typically corrected relative to the changing angle of the sun. Another method to reduce variability from lighting changes is to normalize the reflectance data using an index that is relative to all three bands:
                              p          t                =                  (                                    P              t                                      (                                                R                  t                                +                                  G                  t                                +                                  B                  t                                            )                                )                                    eq        .                                  ⁢                  [          1          ]                    where p is the normalized reflectance value, i.e. pε{r, g, b}, P is the reflectance measurement from a photo active sensor and is an element of {R,G,B}, and t represents the measurement taken at time t.
Normalized spectral data are often classified into useful information using pattern recognition methods, which are derived from statistical techniques. A common technique is linear discriminant analysis used when the classes are assumed to have the same covariance. The classifying equation (Langrebe, 2003, Chapter 3. Pattern Recognition in Remote Sensing in Signal Theory Methods in Multispectral Remote Sensing. Hoboken, N.J.: Wiley & Sons, Inc. p. 91-111) can be represented by:
                                          f            k                    ⁡                      (                          x              t                        )                          =                                            (                                                x                  t                                -                                  μ                  k                                            )                        T                    ⁢                                                    ∑                k                                            -                1                                      ⁢                          (                                                x                  t                                -                                  μ                  k                                            )                                                          eq        .                                  ⁢                  [          2          ]                    where xt is the sample vector (rt, gt, bt) of class k taken at time t, uk is the mean vector of class k, and Σk is the pooled covariance matrix of class k.
Korobov and Railyan (1993, Remote Sensing Environ. 43:1-10) used discriminant analysis to treat sets of spectral and plant measurements as dependent variables and derived correlations between pairs of linear combinations and phytometric variables such as plant height, plant density and percent cover. Miller and Delwiche [1991, ASAE. 34(6): 2509-2515] used linear discriminant analysis to categorize different types of peach surface defects using a radiometer with detectors filtering radiation in the 350 to 1200 nm range, and Wu et al. (1996, Analysis Chimica Acta, 329:257-265) looked at differences in results between linear, quadratic, and regularized discriminant analysis to classify NIR data.
While images from satellites are available that do contain bands in the visible, NIR, and thermal infrared ranges, the advantage of ground-based spectral radiometers is that the user has the ability to control spatial and temporal resolution; additionally ground-based measurements are unaffected by cloud cover and atmospheric light filtering. Furthermore, because it is impractical to deploy wired sensors along a sprinkler lateral whose length is commonly a quarter-mile or longer, wireless sensing devices are needed to commercialize the integration of multi-band radiometers onto a moving sprinkler system (O'Shaughnessy and Evett, 2010, ibid).
A radiometer viewing a cropped surface will likely have both soil and vegetation in its field of view (i.e., sensor footprint). This is especially true for row crops early in the season before the crop canopy completely covers the soil. Furthermore, the soil and vegetation will contain both sunlit and shaded components. The sunlit and shaded components of soil and vegetation have different reflectance and radiometric temperature responses, which can be exploited to estimate crop biophysical characteristics (Fitzgerald et al., 2005, Remote Sens. Environ., 97:526-539). Also, most radiometers used in agricultural applications are deployed such that the cropped surface is viewed at an oblique angle, resulting in the sensor footprint having an elliptical shape. For row crops with partial cover, the proportion of vegetation appearing in a sensor footprint depends on the radiometer deployment height, distance to the crop row, zenith view angle, azimuth view angle relative to the crop row, crop canopy height, width, leaf area index, and canopy architecture (usually quantified in terms of a leaf angle distribution function; Campbell and Norman, 1998, An introduction to environmental biophysics. 2nd ed. Springer-Verlag, New York). Furthermore, the proportion of sunlit and shaded soil and vegetation depends on these factors and on the zenith and azimuth angles of the sun. However, leaf area index can be quantified using multi-band data (Vaesen et al., 2001, Field Crops Research, 69:13-25; Gitelson, 2004, J. Plant Physiology, 161:165-173). The normalized difference vegetative index (NDVI) developed by Rouse et al. (1973, Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS symposium, NASA Sp-351I, 309-317) is often used to estimate leaf area index. The index ranges from near 0 to 1 and is calculated using equation 3:
                    NDVI        =                              (                                          NIR                λ                            -                              Red                λ                                      )                                (                                          NIR                λ                            +                              Red                λ                                      )                                              eq        .                                  ⁢                  [          3          ]                    where reflectance in the NIR region (NIRλ) was measured within the bandwidth of 790-970 nm, and reflectance in the red region (Redλ) was measured within the 650-690 nm bandwidth. Values of NDVI closer to zero indicate more soil is viewed, and values nearer to unity indicate that vegetative cover is nearly complete or is complete.
However, despite these and other advances, the need remains for improved sensors for measuring plant canopy temperature and spectral reflectance, and algorithms to differentiate good or acceptable data from unacceptable data, address the influences of sunlit and shaded soil and canopy on temperature data, detect diseased crops, and manage irrigation.